A data-driven framework for air quality sensor networks

In this article, we present our research vision of a framework for obtaining quality data in air quality monitoring networks using low-cost sensors (LCSs). The use of LCS networks is gaining increasing acceptance in many IoT air quality applications. However, data quality and reliability issues are...

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Detalles Bibliográficos
Autores: Ferrer Cid, Pau|||0000-0003-2112-8516, Paredes Ahumada, Juan Antonio, Allka, Xhensilda|||0000-0003-2187-9985, Guerrero Zapata, Manel|||0000-0002-7983-2525, Barceló Ordinas, José María|||0000-0002-9738-2425, García Vidal, Jorge|||0000-0001-5969-1182
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/408849
Acceso en línea:https://hdl.handle.net/2117/408849
https://dx.doi.org/10.1109/IOTM.001.2300112
Access Level:acceso abierto
Palabra clave:Internet of things
Machine learning
Air quality
Sensor networks
Data quality
Internet de les coses
Aprenentatge automàtic
Aire -- Qualitat
Xarxes de sensors
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Descripción
Sumario:In this article, we present our research vision of a framework for obtaining quality data in air quality monitoring networks using low-cost sensors (LCSs). The use of LCS networks is gaining increasing acceptance in many IoT air quality applications. However, data quality and reliability issues are a major barrier to widespread adoption, which means that the pre-processing tasks that are critical to achieving the required levels of data quality are crucial aspects of LCS network designs. The proposed framework takes advantage of a layered architecture, which has also proven useful in other fields, and from which we show the challenges and state-of-the-art techniques for obtaining quality data. In addition, we show its usefulness in application cases, including a real case with data measured by a LCS deployment measuring O 3 in the area of Barcelona, Spain.